949 research outputs found
Categorization of interestingness measures for knowledge extraction
Finding interesting association rules is an important and active research
field in data mining. The algorithms of the Apriori family are based on two
rule extraction measures, support and confidence. Although these two measures
have the virtue of being algorithmically fast, they generate a prohibitive
number of rules most of which are redundant and irrelevant. It is therefore
necessary to use further measures which filter uninteresting rules. Many
synthesis studies were then realized on the interestingness measures according
to several points of view. Different reported studies have been carried out to
identify "good" properties of rule extraction measures and these properties
have been assessed on 61 measures. The purpose of this paper is twofold. First
to extend the number of the measures and properties to be studied, in addition
to the formalization of the properties proposed in the literature. Second, in
the light of this formal study, to categorize the studied measures. This paper
leads then to identify categories of measures in order to help the users to
efficiently select an appropriate measure by choosing one or more measure(s)
during the knowledge extraction process. The properties evaluation on the 61
measures has enabled us to identify 7 classes of measures, classes that we
obtained using two different clustering techniques.Comment: 34 pages, 4 figure
Social Information Processing in Social News Aggregation
The rise of the social media sites, such as blogs, wikis, Digg and Flickr
among others, underscores the transformation of the Web to a participatory
medium in which users are collaboratively creating, evaluating and distributing
information. The innovations introduced by social media has lead to a new
paradigm for interacting with information, what we call 'social information
processing'. In this paper, we study how social news aggregator Digg exploits
social information processing to solve the problems of document recommendation
and rating. First, we show, by tracking stories over time, that social networks
play an important role in document recommendation. The second contribution of
this paper consists of two mathematical models. The first model describes how
collaborative rating and promotion of stories emerges from the independent
decisions made by many users. The second model describes how a user's
influence, the number of promoted stories and the user's social network,
changes in time. We find qualitative agreement between predictions of the model
and user data gathered from Digg.Comment: Extended version of the paper submitted to IEEE Internet Computing's
special issue on Social Searc
Development of an Explainability Scale to Evaluate Explainable Artificial Intelligence (XAI) Methods
Explainable Artificial Intelligence (XAI) is an area of research that develops methods and techniques to make the results of artificial intelligence understood by humans. In recent years, there has been an increased demand for XAI methods to be developed due to model architectures getting more complicated and government regulations requiring transparency in machine learning models. With this increased demand has come an increased need for instruments to evaluate XAI methods. However, there are few, if none, valid and reliable instruments that take into account human opinion and cover all aspects of explainability. Therefore, this study developed an objective, human-centred questionnaire to evaluate all types of XAI methods. This questionnaire consists of 15 items: 5 items asking about the user’s background information and 10 items evaluating the explainability of the XAI method which were based on the notions of explainability. An experiment was conducted (n = 38) which got participants to evaluate one of two XAI methods using the questionnaire. The results from this experiment were used for exploratory factor analysis which showed that the 10 items related to explainability constitute one factor (Cronbach’s α = 0.81). The results were also used to gather evidence of the questionnaire’s construct validity. It is concluded that this 15-item questionnaire has one factor, has acceptable validity and reliability, and can be used to evaluate and compare XAI methods
Topicality and Social Impact: Diverse Messages but Focused Messengers
Are users who comment on a variety of matters more likely to achieve high
influence than those who delve into one focused field? Do general Twitter
hashtags, such as #lol, tend to be more popular than novel ones, such as
#instantlyinlove? Questions like these demand a way to detect topics hidden
behind messages associated with an individual or a hashtag, and a gauge of
similarity among these topics. Here we develop such an approach to identify
clusters of similar hashtags by detecting communities in the hashtag
co-occurrence network. Then the topical diversity of a user's interests is
quantified by the entropy of her hashtags across different topic clusters. A
similar measure is applied to hashtags, based on co-occurring tags. We find
that high topical diversity of early adopters or co-occurring tags implies high
future popularity of hashtags. In contrast, low diversity helps an individual
accumulate social influence. In short, diverse messages and focused messengers
are more likely to gain impact.Comment: 9 pages, 7 figures, 6 table
Towards a theory unifying implicative interestingness measures and critical values consideration in MGK
The present paper shows the possibility and the benefit to compute statistical freshold for the so-called Guillaume-Kenchaff interestingness measure MGK of association rule and compares it with other measures as Confidence, Lift and Lovinger’s one. Afterwards, it proposes a theory of normalized interestingness measure unifying a set of rule quality measures in a binary context and being surprisingly centered on MGK
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